Detecting Emotional Context for Safer Digital Mental Health Agents
Digital tools for mental health show great promise, but concerns arise when they fail to recognize the user state. We train a classifier to detect the emotional context of dialogs among 6 categories, achieving 78% accuracy on top choice. Importantly greatest areas of confusion (excited-hopeful, angry-sad) are not of the most unsafe kind. Such a classifier could serve as a resource to the dialog managers of future digital mental health agents.